Potential endpoints for clinical trials in premanifest and early Huntington's disease in the TRACK-HD study: analysis of 24 month observational data

Tabrizi, Sarah J.; Reilmann, Ralf; Roos, R.A.C.; Dürr, Alexandra; Leavitt, Blair R.; Owen, Gail; Jones, Rebecca; Johnson, Hans J.; Craufurd, David; Hicks, Stephen L.; Kennard, Christopher; Landwehrmeyer, G. Bernhard; Stout, Julie C.; Borowsky, Beth; Scahill, Rachael I.; Frost, Chris; Langbehn, Douglas R.

doi:10.1016/s1474-4422(11)70263-0

Cited by 490 publications

(539 citation statements)

References 41 publications

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“…Crucially, when caudate volume was used to retrospectively predict those patients who were within 5 years of clinical diagnosis (Fig 5), this measure was seen to be a robust and individualized identifier of real‐life clinical diagnosis. These findings accord and extend existing work,9, 19, 20, 21 providing additional support for the use of caudate volume as a reliable estimate of disease proximity and making it a potentially useful biomarker.…”

Section: Discussionsupporting

confidence: 88%

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

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ObjectiveHuntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD.MethodA multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.ResultsClassification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.InterpretationWe propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543

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Section: Discussionsupporting

confidence: 88%

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

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“…CSF has recently been associated with seeding aggregation of mutant Huntingtin,35 and various components have been posited for use as clinical biomarkers 36. The EBM then predicts changes in the amygdala, optic chiasm, and third ventricle.…”

Section: Discussionmentioning

confidence: 99%

An image‐based model of brain volume biomarker changes in Huntington's disease

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

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ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

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“…It is likely that the white matter changes in the earlyHD group begin many years prior to diagnosis but are not necessarily detectable in comparisons with non‐CAG expanded individuals. There is robust evidence to suggest that macrostructural white matter changes in the cortex occur prior to those in the gray matter with similar white matter microstructural changes (Paulsen et al, 2010; Tabrizi et al, 2012; Wu et al, 2017). …”

Section: Discussionmentioning

confidence: 99%

Natural biological variation of white matter microstructure is accentuated in Huntington's disease

Gregory

Crawford

Seunarine

et al. 2018

Human Brain Mapping

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Huntington's disease (HD) is a monogenic neurodegenerative disorder caused by a CAG‐repeat expansion in the Huntingtin gene. Presence of this expansion signifies certainty of disease onset, but only partly explains age at which onset occurs. Genome‐wide association studies have shown that naturally occurring genetic variability influences HD pathogenesis and disease onset. Investigating the influence of biological traits in the normal population, such as variability in white matter properties, on HD pathogenesis could provide a complementary approach to understanding disease modification. We have previously shown that while white matter diffusivity patterns in the left sensorimotor network were similar in controls and HD gene‐carriers, they were more extreme in the HD group. We hypothesized that the influence of natural variation in diffusivity on effects of HD pathogenesis on white matter is not limited to the sensorimotor network but extends to cognitive, limbic, and visual networks. Using tractography, we investigated 32 bilateral pathways within HD‐related networks, including motor, cognitive, and limbic, and examined diffusivity metrics using principal components analysis. We identified three independent patterns of diffusivity common to controls and HD gene‐carriers that predicted HD status. The first pattern involved almost all tracts, the second was limited to sensorimotor tracts, and the third encompassed cognitive network tracts. Each diffusivity pattern was associated with network specific performance. The consistency in diffusivity patterns across both groups coupled with their association with disease status and task performance indicates that naturally‐occurring patterns of diffusivity can become accentuated in the presence of the HD gene mutation to influence clinical brain function.

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Potential endpoints for clinical trials in premanifest and early Huntington's disease in the TRACK-HD study: analysis of 24 month observational data

Cited by 490 publications

References 41 publications

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

An image‐based model of brain volume biomarker changes in Huntington's disease

Natural biological variation of white matter microstructure is accentuated in Huntington's disease

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